diff --git a/docs/utilities/batch.md b/docs/utilities/batch.md
index aa284e7f38b..ca4606e0f40 100644
--- a/docs/utilities/batch.md
+++ b/docs/utilities/batch.md
@@ -5,13 +5,13 @@ description: Utility
 
 The SQS batch processing utility provides a way to handle partial failures when processing batches of messages from SQS.
 
-**Key Features**
+## Key Features
 
 * Prevent successfully processed messages being returned to SQS
 * Simple interface for individually processing messages from a batch
 * Build your own batch processor using the base classes
 
-**Background**
+## Background
 
 When using SQS as a Lambda event source mapping, Lambda functions are triggered with a batch of messages from SQS.
 
@@ -25,35 +25,76 @@ are returned to the queue.
 
     More details on how Lambda works with SQS can be found in the [AWS documentation](https://docs.aws.amazon.com/lambda/latest/dg/with-sqs.html)
 
+## Getting started
 
-**IAM Permissions**
+### IAM Permissions
 
-This utility requires additional permissions to work as expected. Lambda functions using this utility require the `sqs:DeleteMessageBatch` permission.
+Before your use this utility, your AWS Lambda function must have `sqs:DeleteMessageBatch` permission to delete successful messages directly from the queue.
 
-## Processing messages from SQS
+> Example using AWS Serverless Application Model (SAM)
 
-You can use either **[sqs_batch_processor](#sqs_batch_processor-decorator)** decorator, or **[PartialSQSProcessor](#partialsqsprocessor-context-manager)** as a context manager.
+=== "template.yml"
+    ```yaml hl_lines="2-3 12-15"
+    Resources:
+	  MyQueue:
+		Type: AWS::SQS::Queue
 
-They have nearly the same behaviour when it comes to processing messages from the batch:
+      HelloWorldFunction:
+        Type: AWS::Serverless::Function
+        Properties:
+          Runtime: python3.8
+          Environment:
+            Variables:
+              POWERTOOLS_SERVICE_NAME: example
+		  Policies:
+		    - SQSPollerPolicy:
+			    QueueName:
+				  !GetAtt MyQueue.QueueName
+    ```
 
-* **Entire batch has been successfully processed**, where your Lambda handler returned successfully, we will let SQS delete the batch to optimize your cost
-* **Entire Batch has been partially processed successfully**, where exceptions were raised within your `record handler`, we will:
-    - **1)** Delete successfully processed messages from the queue by directly calling `sqs:DeleteMessageBatch`
-    - **2)** Raise `SQSBatchProcessingError` to ensure failed messages return to your SQS queue
+### Processing messages from SQS
 
-The only difference is that **PartialSQSProcessor** will give you access to processed messages if you need.
+You can use either **[sqs_batch_processor](#sqs_batch_processor-decorator)** decorator, or **[PartialSQSProcessor](#partialsqsprocessor-context-manager)** as a context manager if you'd like access to the processed results.
 
-## Record Handler
+You need to create a function to handle each record from the batch - We call it `record_handler` from here on.
 
-Both decorator and context managers require an explicit function to process the batch of messages - namely `record_handler` parameter.
+=== "Decorator"
 
-This function is responsible for processing each individual message from the batch, and to raise an exception if unable to process any of the messages sent.
+    ```python hl_lines="3 6"
+    from aws_lambda_powertools.utilities.batch import sqs_batch_processor
 
-**Any non-exception/successful return from your record handler function** will instruct both decorator and context manager to queue up each individual message for deletion.
+    def record_handler(record):
+        return do_something_with(record["body"])
 
-### sqs_batch_processor decorator
+    @sqs_batch_processor(record_handler=record_handler)
+    def lambda_handler(event, context):
+        return {"statusCode": 200}
+    ```
+=== "Context manager"
 
-When using this decorator, you need provide a function via `record_handler` param that will process individual messages from the batch - It should raise an exception if it is unable to process the record.
+    ```python hl_lines="3 9 11-12"
+    from aws_lambda_powertools.utilities.batch import PartialSQSProcessor
+
+    def record_handler(record):
+        return_value = do_something_with(record["body"])
+        return return_value
+
+    def lambda_handler(event, context):
+        records = event["Records"]
+        processor = PartialSQSProcessor()
+
+        with processor(records, record_handler) as proc:
+            result = proc.process()  # Returns a list of all results from record_handler
+
+        return result
+    ```
+
+!!! tip
+	**Any non-exception/successful return from your record handler function** will instruct both decorator and context manager to queue up each individual message for deletion.
+
+	If the entire batch succeeds, we let Lambda to proceed in deleting the records from the queue for cost reasons.
+
+### Partial failure mechanics
 
 All records in the batch will be passed to this handler for processing, even if exceptions are thrown - Here's the behaviour after completing the batch:
 
@@ -61,29 +102,26 @@ All records in the batch will be passed to this handler for processing, even if
 * **Any unprocessed messages detected**, we will raise `SQSBatchProcessingError` to ensure failed messages return to your SQS queue
 
 !!! warning
-    You will not have accessed to the <strong>processed messages</strong> within the Lambda Handler - all processing logic will and should be performed by the <code>record_handler</code> function.
+    You will not have accessed to the **processed messages** within the Lambda Handler.
 
-=== "app.py"
+	All processing logic will and should be performed by the `record_handler` function.
 
-    ```python
-    from aws_lambda_powertools.utilities.batch import sqs_batch_processor
+## Advanced
 
-    def record_handler(record):
-        # This will be called for each individual message from a batch
-        # It should raise an exception if the message was not processed successfully
-        return_value = do_something_with(record["body"])
-        return return_value
+### Choosing between decorator and context manager
 
-    @sqs_batch_processor(record_handler=record_handler)
-    def lambda_handler(event, context):
-        return {"statusCode": 200}
-    ```
+They have nearly the same behaviour when it comes to processing messages from the batch:
 
-### PartialSQSProcessor context manager
+* **Entire batch has been successfully processed**, where your Lambda handler returned successfully, we will let SQS delete the batch to optimize your cost
+* **Entire Batch has been partially processed successfully**, where exceptions were raised within your `record handler`, we will:
+    - **1)** Delete successfully processed messages from the queue by directly calling `sqs:DeleteMessageBatch`
+    - **2)** Raise `SQSBatchProcessingError` to ensure failed messages return to your SQS queue
+
+The only difference is that **PartialSQSProcessor** will give you access to processed messages if you need.
 
-If you require access to the result of processed messages, you can use this context manager.
+### Accessing processed messages
 
-The result from calling `process()` on the context manager will be a list of all the return values from your `record_handler` function.
+Use `PartialSQSProcessor` context manager to access a list of all return values from your `record_handler` function.
 
 === "app.py"
 
@@ -91,11 +129,7 @@ The result from calling `process()` on the context manager will be a list of all
     from aws_lambda_powertools.utilities.batch import PartialSQSProcessor
 
     def record_handler(record):
-        # This will be called for each individual message from a batch
-        # It should raise an exception if the message was not processed successfully
-        return_value = do_something_with(record["body"])
-        return return_value
-
+        return do_something_with(record["body"])
 
     def lambda_handler(event, context):
         records = event["Records"]
@@ -108,7 +142,7 @@ The result from calling `process()` on the context manager will be a list of all
         return result
     ```
 
-## Passing custom boto3 config
+### Passing custom boto3 config
 
 If you need to pass custom configuration such as region to the SDK, you can pass your own [botocore config object](https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html) to
 the `sqs_batch_processor` decorator:
@@ -159,14 +193,15 @@ the `sqs_batch_processor` decorator:
     ```
 
 
-## Suppressing exceptions
+### Suppressing exceptions
 
 If you want to disable the default behavior where `SQSBatchProcessingError` is raised if there are any errors, you can pass the `suppress_exception` boolean argument.
 
 === "Decorator"
 
-    ```python hl_lines="2"
-    ...
+    ```python hl_lines="3"
+    from aws_lambda_powertools.utilities.batch import sqs_batch_processor
+
     @sqs_batch_processor(record_handler=record_handler, config=config, suppress_exception=True)
     def lambda_handler(event, context):
         return {"statusCode": 200}
@@ -174,15 +209,16 @@ If you want to disable the default behavior where `SQSBatchProcessingError` is r
 
 === "Context manager"
 
-    ```python hl_lines="2"
-    ...
+    ```python hl_lines="3"
+    from aws_lambda_powertools.utilities.batch import PartialSQSProcessor
+
     processor = PartialSQSProcessor(config=config, suppress_exception=True)
 
     with processor(records, record_handler):
         result = processor.process()
     ```
 
-## Create your own partial processor
+### Create your own partial processor
 
 You can create your own partial batch processor by inheriting the `BasePartialProcessor` class, and implementing `_prepare()`, `_clean()` and `_process_record()`.
 
@@ -192,11 +228,9 @@ You can create your own partial batch processor by inheriting the `BasePartialPr
 
 You can then use this class as a context manager, or pass it to `batch_processor` to use as a decorator on your Lambda handler function.
 
-**Example:**
-
 === "custom_processor.py"
 
-    ```python
+    ```python hl_lines="3 9 24 30 37 57"
     from random import randint
 
     from aws_lambda_powertools.utilities.batch import BasePartialProcessor, batch_processor
@@ -223,14 +257,12 @@ You can then use this class as a context manager, or pass it to `batch_processor
         def _prepare(self):
             # It's called once, *before* processing
             # Creates table resource and clean previous results
-            # E.g.:
             self.ddb_table = boto3.resource("dynamodb").Table(self.table_name)
             self.success_messages.clear()
 
         def _clean(self):
             # It's called once, *after* closing processing all records (closing the context manager)
             # Here we're sending, at once, all successful messages to a ddb table
-            # E.g.:
             with ddb_table.batch_writer() as batch:
                 for result in self.success_messages:
                     batch.put_item(Item=result)
@@ -239,7 +271,6 @@ You can then use this class as a context manager, or pass it to `batch_processor
             # It handles how your record is processed
             # Here we're keeping the status of each run
             # where self.handler is the record_handler function passed as an argument
-            # E.g.:
             try:
                 result = self.handler(record) # record_handler passed to decorator/context manager
                 return self.success_handler(record, result)
@@ -260,3 +291,24 @@ You can then use this class as a context manager, or pass it to `batch_processor
     def lambda_handler(event, context):
         return {"statusCode": 200}
     ```
+
+### Integrating exception handling with Sentry.io
+
+When using Sentry.io for error monitoring, you can override `failure_handler` to include to capture each processing exception:
+
+> Credits to [Charles-Axel Dein](https://github.com/awslabs/aws-lambda-powertools-python/issues/293#issuecomment-781961732)
+
+=== "sentry_integration.py"
+
+	```python hl_lines="4 7-8"
+	from typing import Tuple
+
+	from aws_lambda_powertools.utilities.batch import PartialSQSProcessor
+	from sentry_sdk import capture_exception
+
+	class SQSProcessor(PartialSQSProcessor):
+		def failure_handler(self, record: Event, exception: Tuple) -> Tuple:  # type: ignore
+			capture_exception()  # send exception to Sentry
+			logger.exception("got exception while processing SQS message")
+			return super().failure_handler(record, exception)  # type: ignore
+	```